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Pedestrian Detection Based on Informed Haar-like Features and Switchable Deep Network

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DOI: 10.23977/isspj.2016.11002 | Downloads: 79 | Views: 6593

Author(s)

Gu Lingkang 1

Affiliation(s)

1 College of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China

Corresponding Author

Gu Lingkang

ABSTRACT

As pedestrians usually appear up-right in image or video data, we therefore employ a statistical model of the up-right human body where the head, the upper body, and the lower body are treated as three distinct components. As we incorporate different kinds of low-level measurements, the resulting multi-modal & multi-channel Haar-like features represent characteristic differences between parts of the human body yet are robust against variations in clothing or environmental settings. Then we use a Switchable Deep Network(SDN) for pedestrian detection. The SDN automatically learns features of different body parts. Experimental results on many pedestrian datasets show that the proposed algorithm significantly improves the detection rates at 0.1FPPI compared with the state-of-the-art domain adaptation methods and that it is robust and accurate against cluttered dynamical background, occlusion and the object deformation.

KEYWORDS

Haar-like features, feature extraction, pedestrian detection, Switchable Deep Network

CITE THIS PAPER

Lingkang, G. (2016) Pedestrian Detection Based on Informed Haar-like Features and Switchable Deep Network. Information Systems and Signal Processing Journal (2016) 1: 7-11.

REFERENCES

[1] Gu Lingkang. Fast pedestrian detection based on feature of local model. Journal of Computational Methods in Sciences and Engineering, v 15, n 3, p 387-393, August 3, 2015.
[2] K. Sohn, G. Zhou, C. Lee, and H. Lee. Learning and selecting features jointly with point-wise gated Boltzmann machines.ICML, 2013.
[3] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. CVPR, 2015.
[4] P. Viola, M. J. Jones, and D. Snow. Detecting pedestrians using patterns of motion and appearance. IJCV, 63(2):153–161, 2005.
[5] P. Luo, L. Lin, and H. Chao. Learning shape detector by quantizing curve segments with multiple distance metrics.ECCV, 2010.
[6] P. Doll´ar, Z. Tu, P. Perona, and S. Belongie. Integral channel features. BMVC, 2009.
[7] X. Wang, X. Han, and S. Yan. An hog-lbp human detector with partial occlusion handling. CVPR, 2009.
[8] L. Bourdev and J. Malik. Poselets: Body part detectors trained using 3d human pose annotations. In ICCV, 2009.
[9] P. Doll′ar, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: an evaluation of the state of the art. IEEE Trans.PAMI, 34(4):743–761, 2011. 1, 5
[10] Shanshan Zhang, Christian Bauckhage, Armin B. Cremers. Informed Haar-like Features Improve Pedestrian Detection. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, 126, 947-954.
[11] J. Gall, A. Yao, N. Razavi, L. V. Gool, and V. Lempitsky. Hough forests for object detection, tracking, and action recognition. IEEE Trans. PAMI, 33(11):2188–2202, 2011. 2.

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